English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Anomaly detection in high dimensional data is becoming a fundamental research problem that has various applications in the real world. However, many existing anomaly detection techniques fail to retain sufficient accuracy due to so-called “big data” characterised by high-volume, and high-velocity data generated by variety of sources. This phenomenon of having both problems together can be referred to the “curse of big dimensionality,” that affect existing techniques in terms of both performance and accuracy. To address this gap and to understand the core problem, it is necessary to identify the unique challenges brought by the anomaly detection with both high dimensionality and big data problems. Hence, this survey aims to document the state of anomaly detection in high dimensional big data by representing the unique challenges using a triangular model of vertices: the problem (big dimensionality), techniques/algorithms (anomaly detection), and tools (big data applications/frameworks). Authors’ work that fall directly into any of the vertices or closely related to them are taken into consideration for review. Furthermore, the limitations of traditional approaches and current strategies of high dimensional data are discussed along with recent techniques and applications on big data required for the optimization of anomaly detection.

A comprehensive survey of anomaly detection techniques for high dimensional big data